Bridging Age, Body Mass Index, and Gait Variability: Differential Effects on Stride Interval Dynamics
Presenter Type
UNO Graduate Student (Doctoral)
Major/Field of Study
Biomechanics
Author ORCID Identifier
0000-0003-2433-0525
Advisor Information
Dr. Aaron D Likens/Biomechanics, Assistant Professor
Location
CEC RM #201/205/209
Presentation Type
Poster
Start Date
22-3-2024 10:30 AM
End Date
22-3-2024 11:45 AM
Abstract
Gait variability is a critical aspect of human mobility, impacting daily activities and overall quality of life. Understanding the factors contributing to gait variability is essential for promoting healthy aging and preventing movement impairments. This study explored the interplay between age, body mass index (BMI), and their collective impact on gait variability, utilizing the Hurst exponent (H) to quantify the autocorrelation of stride intervals, reflecting gait stability. We analyzed data from 97 participants divided into three age groups: young adults (19-35 years old), middle-aged adults (36-55 years old), and older adults (above 55 years). Gait kinematics were recorded using 16 Noraxon Ultium Motion™ inertial measurement units over two sessions, comprising nine 4-minute walks on a 200-meter track. A linear mixed-effects model evaluated how age and BMI jointly predict H, with both variables z-score normalized for interpretability. Key findings revealed a significant BMI × age interaction (F(2, 91.52) = 5.6, p = .005), indicating the relationship between BMI and gait variability with age. Simple slope analysis, controlling for age, showed that for young adults, one standard deviation (SD) increase in BMI predicted a one SD increase in H. Conversely, for middle-aged adults, a one SD increase in BMI predicted a .28 SD decrease in H, suggesting a reversal in the relationship. No detectable relationship between BMI and H was observed for older adults. The conditional R² of .602 for the final model indicates that both fixed and random effects account for a considerable portion of the variability in gait dynamics. In contrast, the marginal R² of .080 highlights the substantial role of individual differences. These results suggest that the influence of BMI on gait dynamics is nuanced, likely reflecting physiological changes across the lifespan. The positive relationship observed in young adults could be attributed to greater muscle mass, which may afford better support and stability. However, this relationship reverses in middle age, possibly due to the combination of decreased muscle mass and increased fat mass negatively affecting gait. For older adults, the absence of a detectable relationship suggests that other age-related factors may play a more dominant role in gait variability. This study underscores the complex dynamics between age, BMI, and gait variability and highlights the importance of considering individual differences in understanding gait patterns. Future research should further explore these relationships and their implications for developing targeted interventions to maintain mobility and reduce fall risk across the lifespan.
Bridging Age, Body Mass Index, and Gait Variability: Differential Effects on Stride Interval Dynamics
CEC RM #201/205/209
Gait variability is a critical aspect of human mobility, impacting daily activities and overall quality of life. Understanding the factors contributing to gait variability is essential for promoting healthy aging and preventing movement impairments. This study explored the interplay between age, body mass index (BMI), and their collective impact on gait variability, utilizing the Hurst exponent (H) to quantify the autocorrelation of stride intervals, reflecting gait stability. We analyzed data from 97 participants divided into three age groups: young adults (19-35 years old), middle-aged adults (36-55 years old), and older adults (above 55 years). Gait kinematics were recorded using 16 Noraxon Ultium Motion™ inertial measurement units over two sessions, comprising nine 4-minute walks on a 200-meter track. A linear mixed-effects model evaluated how age and BMI jointly predict H, with both variables z-score normalized for interpretability. Key findings revealed a significant BMI × age interaction (F(2, 91.52) = 5.6, p = .005), indicating the relationship between BMI and gait variability with age. Simple slope analysis, controlling for age, showed that for young adults, one standard deviation (SD) increase in BMI predicted a one SD increase in H. Conversely, for middle-aged adults, a one SD increase in BMI predicted a .28 SD decrease in H, suggesting a reversal in the relationship. No detectable relationship between BMI and H was observed for older adults. The conditional R² of .602 for the final model indicates that both fixed and random effects account for a considerable portion of the variability in gait dynamics. In contrast, the marginal R² of .080 highlights the substantial role of individual differences. These results suggest that the influence of BMI on gait dynamics is nuanced, likely reflecting physiological changes across the lifespan. The positive relationship observed in young adults could be attributed to greater muscle mass, which may afford better support and stability. However, this relationship reverses in middle age, possibly due to the combination of decreased muscle mass and increased fat mass negatively affecting gait. For older adults, the absence of a detectable relationship suggests that other age-related factors may play a more dominant role in gait variability. This study underscores the complex dynamics between age, BMI, and gait variability and highlights the importance of considering individual differences in understanding gait patterns. Future research should further explore these relationships and their implications for developing targeted interventions to maintain mobility and reduce fall risk across the lifespan.